Representation learning models usually aim to build representations of input data by mining its inherent characteristics [1]}.
However, the raw data (e.g., user-generated web data) may encode biases related to some sensitive attributes such as demographics [2]}.
The models learned on such data may also inherit these biases and generate biased representations [3]}.
For example, researchers have found that in many canonical word embeddings such as Word2vec [4]} and GloVe [5]}, the word “doctor” has a closer relation to “male” while “nurse” has a smaller distance to “female” [6]}.
Such “stereotypes” of models may lead to biased decisions that are unfair to groups with certain sensitive attributes [7]}.
Thus, fair representation learning, which aims to learn representations that are free from the influence of targeted sensitive attributes, is important to build responsible intelligent web systems and make fair automatic decisions [8]}.
